Systems, devices and methods for distributed content pre-fetching in mobile communication networks

ABSTRACT

There are disclosed systems, devices, and methods for distributing pre-fetch data. A parent node obtains pre-fetch data comprising at least one of: i) data expected to be of interest to a particular user, pre-fetched by the parent node from at least one data source; and (ii) at least one identifier identifying data expected to be of interest to the particular user, for pre-fetching the identified data at a child node. The parent node selects first and second subsets of the pre-fetch data for transmission, respectively, to first and second child nodes, the selecting based on at least a predicted future location of the particular user and a respective geographic location of the first and second child nodes; and transmits the first and second subsets of the pre-fetch data, respectively, to the first and second child nodes.

FIELD

This relates to data communications, and more particularly, to contentpre-fetching in mobile communication networks.

BACKGROUND

In recent years, access to wireless data communication has proliferated.For example, coverage of mobile telecommunication networks (e.g., 3G,4G, LTE, etc.) and WiFi networks has steadily expanded. This has createdan expectation, in some users, of continuous and instant wirelessconnectivity, and being able to access content by way of wireless datacommunication at all times. However, despite advances in wireless datacommunication, many wireless and backhaul access links are unreliable,low-rate, or high latency, and many coverage gaps still exist. As aresult, users may fail to obtain content data when desired.

Pre-fetching of content data has been employed to improve Quality ofExperience in various aspects. For example, content data may bepre-fetched from remote content data sources to access points proximatea mobile device user. However, when the mobile device user moves out ofrange of that access point, a second access point must take overpre-fetching responsibilities. The second access point must pre-fetchcontent data from the remote content data sources. However, if the linksbetween the second access point and the remote content data sources areunreliable or low-rate, then the content data may not be retrieved intime.

Accordingly, there exists a need for systems, devices, and methods thataddress at least some of the above-noted shortcomings.

SUMMARY

In accordance with an aspect, there is provided a method at a parentnode for distributing pre-fetch data to at least two child nodes. Themethod includes: obtaining pre-fetch data comprising at least one of:(i) data expected to be of interest to a particular user, pre-fetched bythe parent node from at least one data source by way of at least onedata network; and (ii) at least one identifier identifying data expectedto be of interest to the particular user, for pre-fetching theidentified data at at least one of the child nodes; selecting a firstsubset and a second subset of the pre-fetch data for transmission,respectively, to a first child node and a second child node of the atleast two child nodes, the selecting based on at least a predictedfuture location of the particular user and a respective geographiclocation of the first and second child nodes; and transmitting the firstsubset and the second subset of the pre-fetch data, respectively, to thefirst child node and the second child node by way of the at least onedata network.

In accordance with another aspect, there is provided a network node fordistributing pre-fetch data. The node includes: a network interface forinterconnection with at least two child nodes by at least one datanetwork; and at least one processor in communication with the networkinterface. The at least one processor is configured to: obtain pre-fetchdata comprising at least one of: (i) data expected to be of interest toa particular user, pre-fetched by the network node from at least onedata source by way of the network interface; and (ii) at least oneidentifier identifying data expected to be of interest to the particularuser, for pre-fetching the identified data at at least one of the childnodes; select a first subset and a second subset of the pre-fetch datafor transmission, respectively, to a first child node and a second childnode of the at least two child nodes, the selecting based on at least apredicted future location of the particular user and a respectivegeographic location of the first and second child nodes; and transmit,by way of the network interface, the first subset and the second subsetof the pre-fetch data, respectively, to the first child node and thesecond child node.

Many further features and combinations thereof concerning the presentimprovements will appear to those skilled in the art following a readingof the instant disclosure.

DESCRIPTION OF THE FIGURES

In the figures,

FIG. 1 is a schematic diagram of a data communication system includingroot nodes and child nodes, according to an embodiment;

FIG. 2 is a high-level block diagram of the root node of FIG. 1,according to an embodiment;

FIG. 3 is a schematic diagram showing selection of portions of contentdata to be sent from a root node to child nodes, according to anembodiment;

FIG. 4 is a schematic diagram of a data communication system includingroot nodes and child nodes arranged in a four-level hierarchy, accordingto an embodiment;

FIG. 5 is a data-flow diagram showing exchange of data between rootnodes and child nodes in the system of FIG. 4, according to anembodiment;

FIG. 6 and FIG. 7 are flowcharts showing example operation at a rootnode, according to an embodiment;

FIG. 8 is a flowchart showing example operation at a child node,according to an embodiment; and

FIG. 9 is a high-level block diagram of an example device forimplementing nodes, according to an embodiment.

These drawings depict example embodiments for illustrative purposes, andvariations, alternative configurations, alternative components andmodifications may be made to the disclosed embodiments.

DETAILED DESCRIPTION

FIG. 1 illustrates a data communication system 10 that performs per-usercontent pre-fetching.

System 10 performs content pre-fetching using a distributed set of nodesinterconnected by one or more data communication networks. The set ofnodes includes nodes distributed geographically at locations proximatepredicted future locations of a particular user. For example, the set ofnodes may include nodes located along a travel route predicted for aparticular user.

In an aspect, multiple nodes may each perform user mobility prediction,and nodes closer to the user may produce more accurate or more timelypredictions, as detailed below. Such predictions may be shared amongstthe nodes, e.g., to provide pre-fetched data at a location proximate toa predicted future location of the user.

In an aspect, these nodes cooperate in manners detailed herein toperform content pre-fetching, and to distribute pre-fetched content dataamongst the nodes such that at least a portion of content data isavailable at a node proximate the user as the user moves from locationto location. The pre-fetched content data made available at each nodemay be tailored to suit a predicted situation of the user when proximatethat node, e.g., activities being performed, applications beingexecuted, etc.

Distributing pre-fetched content data to nodes proximate predictedfuture locations for a particular user facilitates ready access by theuser's mobile device to at least some of the content data, even when thedevice's network connectivity may be limited, e.g., when the links tocontent data sources may be unreliable or low-rate.

The pre-fetched content data may be transmitted from one of thedistributed nodes to the particular user's mobile device for immediateor future consumption.

In another aspect, the nodes of system 10 may cooperate in mannersdetailed herein to perform per-user content interest prediction toidentify content data expected to be of interest to particular mobiledevice users, and to distribute a list including one or more identifiersof the content data expected to be of interest to a particular useramongst the nodes such that at least parts of the list are available ata node proximate the user as the user moves from location to location.The content list data made available at each node may be tailored tosuit a predicted situation of the user when proximate that node, e.g.,activities being performed, applications being executed, etc.

In yet another aspect, the nodes of system 10 may cooperate in mannersdetailed herein to perform content discovery to find locations ofcontent data identified in a content list. For example, multiple nodesmay each search for a location of content data closest to that node. Inthis way, content data may be retrieved from a preferred location (e.g.,a location close to the user, or a location having a lower access cost),which may vary as the user moves from location to location. For example,the nodes of system 10 may pre-fetch content data from such a preferredlocation, as found.

In the example embodiment depicted in FIG. 1, system 10 performs contentpre-fetching for a particular user operating a mobile device 200 who istraveling along route 300. As depicted, system 10 includes a root node100 interconnected with a plurality of child nodes, e.g., nodes 150-1,150-2, 150-N, and so on, hereinafter referred to collectively as childnodes 150. Root node 100 is also interconnected with one or more contentdata sources 14.

As detailed herein, in an embodiment root node 100 pre-fetches contentdata from the one or more content data sources 14. Root node 100 thenforwards the pre-fetched content data to particular child nodesproximate predicted future locations of the user. Mobile device 200 maythen pre-fetch the data or retrieve the data when needed from thosechild nodes.

In an embodiment, a set of such nodes (e.g., root node 100 and childnodes 150) is instantiated for each particular user, to perform per-usercontent pre-fetching in manners detailed herein. In this embodiment,each set of nodes is dedicated to the particular user, and serves onlythat particular user.

Root node 100, child nodes 150, and content data sources 14 areinterconnected by one or more data communication networks 8. Networks 8may include packet-switched networks, circuit-switched networks, or acombination thereof. Networks 8 may include wired links, wireless links,or a combination thereof. Networks 8 may include wired access points andwireless access points. Networks 8 may include or be connected to theInternet.

In the depicted embodiment, root node 100 is located in the cloud, whereit may be interconnected by communication links to one or more contentdata sources 14. The links between root node 100 and one or more of thecontent data sources 14 may have deficiencies such as, for example, lowdata rate, high latency, etc. Further, content data sources 14 mayrequire authorization to access the desired content.

Conveniently, pre-fetching according to the depicted embodiment mayimprove Quality of Experience for the user, and particularly in thepresence of such link deficiencies. Further, pre-fetching in combinationwith pre-authorization as described below for the depicted embodimentmay allow certain delays associated with establishing authorizedconnections to be avoided.

Root node 100 maintains a content list for the particular user. Thecontent list includes a plurality of entries, each identifying a contentitem expected to be of interest to the user. Root node 100 may populatethe content list, for example, based on content interests predicted forthe particular user. Root node 100 may also populate the content list,for example, based on content interests expressed by the particular useror indicated by an application executing at the user's mobile device200. Root node 100 updates the list as new content interest predictionsare made, and new content interests are received.

Root node 100 may pre-fetch content data according to content identifiedin this content list. Root node 100 may assign priorities to portions ofcontent data, for example, based on a user mobility prediction, alikelihood that the user will access the content data at a particularlocation, e.g., from a particular child node, etc. Conveniently, in somecases, assigning priorities to portions of the content data, anddistributing portions of the content data according to those priorities,improves the utilization of network resources. For example, highpriority content data may be pre-fetched at a node first, to make thatcontent data available to the user in the face of network congestion,link deficiencies, or storage limitations at a node or at mobile device200.

In an embodiment, device 200 maintains its own content list, and rootnode 100 and device 100 synchronize their respective content lists. Rootnode 100 transmits content list updates to device 200, and receivescontent list updates from device 200. Such updates may be transmitteddirectly to device 200, by way of child nodes 150, or through networksother than networks 8.

Child nodes 150 are established at locations proximate predicted futurelocations of a particular user. In the example embodiment depicted inFIG. 1, the locations of child nodes 150 are selected to be along apredicted travel route 300 of the user. In this embodiment, the user isexpected to be proximate child node 150-1 at time T₁, proximate childnode 150-2 at time T₂, proximate child node 150-N at time T_(N), and soon. A child node 150 may, for example, be established at an edge of awireless networks with which device 200 has connectivity at a particularpoint in time.

In the depicted embodiment, child nodes 150 are instantiated by system10 at desired locations proximate the predicted future locations of theuser. For example, a root node 100 may cause a new child node 150 to beinstantiated by selecting a desired location for the new node, and thentransmitting a request to a device at the selected geographic locationto function as the new node. Child nodes 150 may be instantiated aspredictions of the future locations become available, and prior to theuser's arrival at those locations. For example, child node 150-N (shownin dotted lines) may not exist at time T₁, but may be instantiated bysystem 10 at T_(N-1). System 10 may remove any of child nodes 150 onceit is no longer required, e.g., once a user has traveled out of therange of the child node 150, or if the predicted future locations aredetermined to be incorrect. For example, a root node 100 may cause achild node 150 to be removed by transmitting a request to a device tocease functioning as the child node 150. Thus, each of child nodes 150may exist only temporarily. In this way, child nodes 150 may beinstantiated to service a geographical area that varies according to aparticular user's location. For example, this geographical area maymigrate with the particular user as that user moves from location tolocation.

In another embodiment, a plurality of child nodes 150 may be provided ata plurality of locations, e.g., to span a geographic area, before anypredictions of a user's future location are made.

Child nodes 150 may be located in different wireless networks, e.g., indifferent jurisdictions, operated by different service providers, orconfigured to communicate with mobile devices by way of different RadioAccess Technology (RAT), e.g., WiFi, 3G, 4G, LTE, etc.

Each child node 150 may maintain its own content list. The content listmaintained at a child node 150 may be a subset of the content listmaintained at root node 100 or at device 200. The subset is selectedbased on content expected to be of interest to the user when the user isproximate a respective child node 150.

Each child node 150 may store pre-fetched content data for futuretransmission to mobile device 200. The pre-fetched content data storedat a child node 150 may include data pre-fetched at root node 100 andreceived therefrom. The pre-fetched content data stored at a child node150 may also include data pre-fetched at that child node 150. Each childnode 150 may pre-fetch content according to content identified in itsown content list. Each child node 150 may also pre-fetch content uponreceiving a request, for example, from root node 100 or mobile device200.

The pre-fetched content data stored at each child node 150 is dataexpected to be needed at mobile device 200 when the user is proximatethat child node 150. So, for example, the pre-fetched content datastored at child node 150-1 may include content data expected to beneeded at mobile device 200 at time T₁; the pre-fetched content datastored at child node 150-2 may include content data expected to beneeded at mobile device 200 at time T₂; the pre-fetched content datastored at child node 150-N may include content data expected to beneeded at mobile device 200 at time T_(N), and so on. The content dataexpected to be needed at mobile device 200 at a particular time aredetermined based on predictions of the user's situation at that time,e.g., location, activities being performed, applications being executed,etc., as detailed below.

Conveniently, in the depicted embodiment, because the pre-fetchedcontent data stored at each of child nodes 150-1 and 150-2 may becontrolled and allocated by root node 100, content data may be suppliedto mobile device 200 without user perceivable interruption as the usermoves out of the range of child node 150-1 and into the range of childnode 150-2.

In the absence of control provided by root node 100 in manners describedherein, data may be pre-fetched (e.g., at multiple nodes) unnecessarily.For example, in one example conventional system, when a user moves froma first access point to a second access point, all of the pre-fetchedcontent stored at the first access point may need to be downloaded againat the second access point.

Further, in the depicted embodiment, even though child nodes 150-1 and150-2 may be in two different wireless networks, pre-fetched contentdata may be provided without user perceivable interruption to mobiledevice 200 even when the device transitions from one wireless network toanother. Similarly, even though child nodes 150-1 and 150-2 may usedifferent RAT, pre-fetched content data may be provided to mobile device200 without user perceivable interruption even when the devicetransitions from one RAT to another.

Mobile device 200 may be a mobile phone or any other type of mobiledevice (e.g., a tablet computer or a laptop computer). Device 200includes one or more communication interfaces allowing the device toaccess data communication networks by way of one more RAT.

Mobile device 200 communicates with one or more of child nodes 150,e.g., to receive pre-fetched content data from a child node 150. In anembodiment, device 200 communicates with child nodes 150 directly. Inanother embodiment, device 200 communicates with child nodes 150indirectly, e.g., by way of wireless access points.

As noted, in an embodiment, device 200 maintains its own content list.Device 200 may perform content interest prediction and update itscontent list accordingly. Device 200 may also receive content requestsfrom the user and update its content list accordingly. Device 200synchronizes its content list with the content list at root node 100 byexchanging content list updates. Such updates may be transmitted to rootnode 100 directly, or by way of child nodes 150, or through networksother than networks 8.

FIG. 2 provides a high-level block diagram of root node 100. Asdepicted, node 100 includes a mobility prediction module 102, a contentinterest prediction module 104, a content finder module 106, a contentlist distribution module 108, a pre-fetch module 110, apre-authorization module 112, a content data distribution module 114,and an upload module 116.

Mobility prediction module 102 is configured to perform mobilityprediction for a particular user. For example, mobility predictionmodule 102 may process a variety of data to predict a user's location atparticular future points in times (e.g., at times T₁, T₂, . . . T_(N)).Mobility prediction module 102 may predict a user's future locations,for example, by processing data reflective of the user's currentlocation, trajectory, speed, as may be obtained from onboard sensors(e.g., a GPS sensor) of a mobile device 200 and transmitted therefrom toroot node 100, and data reflective of possible travel routes, e.g., roadmaps obtained from an online mapping service. In an embodiment, mobilityprediction module 102 may also process data reflective of a user'stravel plans, e.g., as obtained from a route planning applicationexecuting at a device 200, or from a server of a travel agency, anairline, or the like.

In an embodiment, mobility prediction module 102 may be configured topredict other aspects of a user's situation at particular future pointsin time including, for example, an activity that will be performed bythe user, an application that will be executed at the user's device 200,etc.

Mobility prediction module 102 may predict aspects of a user's situationbased on, e.g., the user's predicted location. So, for example, mobilityprediction module 102 may predict that a particular user will beworking, shopping, commuting, at home, etc., based on the user'spredicted locations. Mobility prediction module 102 may predict aspectsof a user's situation based on, e.g., the time of day, the day of theweek, to determine whether a future time period falls within workinghours.

Mobility prediction module 102 may also predict aspects of a user'ssituation based on data reflective of the user's current activities, orcurrent applications being executed at the user's mobile device. In onespecific example, mobility prediction module 102 may determine that auser will likely be watching a streaming video in five minutes based ondata indicating that the user is currently watching the video and thevideo has one hour remaining, as may be determined from an videoapplication executing at device 200. In another specific example,mobility prediction module 102 may determine that a user will likely belistening to music in two minutes based on data indicating that the useris currently jogging, e.g., as may be determined from onboard sensors(e.g., gyroscope) of device 200, and based on historical data indicatingthat the user typically listens to music when jogging.

Mobility prediction module 102 may also predict aspects of a user'ssituation based on locations of other users, e.g., to determine when theuser is in a crowd. For example, mobility prediction module 102 maydetermine a user's situation based on determining a common activity ofthe crowd, e.g., that the user is attending a concert, queuing at asupermarket, etc. This common activity may be determined, for example,from content being accessed by other members in the crowd. This commonactivity may be used to identify content of interest to the user, toprioritize content of interest, etc.

In an embodiment, mobility prediction module 102 may implement one ormore conventional mobility prediction algorithms. In an embodiment,mobility prediction module 102 may predict a user future's situationusing a statistical model such as, e.g., a hidden Markov model, whichmay be trained using population data or user-specific data.

Content interest prediction module 104 is configured to perform contentinterest prediction to identify content items expected to be of interestto a particular user. Content interest prediction module 104 may performcontent interest prediction by processing data reflective of a user'spast content consumption (e.g., browsing history, history of viewedvideos), or data reflective of a user's current content consumption(e.g., searches being conducted, articles being read). Content interestprediction module 104 may perform content interest prediction byprocessing data reflective of data consumption behavior of a populationof users, e.g., other users of networks 8. In this way, popular ortrending content may be identified as being of interest to a particularuser.

In an embodiment, content interest prediction module 104 may take intoaccount data reflecting the situations of other users in the populationof users, to more heavily weigh data consumption behavior of other usersin situations similar to the user's predicted future situations. Contentinterest prediction module 104 may also group users by demographiccharacteristics and more heavily weigh data from users having similardemographic characteristics.

Content items may include any type of content data that a user mightaccess in the future. Content items may include publically-availabledata, e.g., webpages, YouTube™ videos, or the like. Content items mayalso include private secured-access data, e.g., incoming e-mails, orDropbox™ files, etc. Access to private data may be facilitated bypre-authorization module 112, as described below.

Content items of interest to the user may also include types of contentdata that are used by device 200 or an application executing at mobiledevice 200. The user of device 200 may not be aware of exchange or useof such content data. For example, content items may include DNStranslations of hostnames to IP addresses, as may be used byapplications executing at device 200.

Content items may be identified at a high-level of generality, e.g.,news, baseball, etc. Content items may be also be identified moreprecisely, e.g., a particular URL, a particular keyword, a particularwebpage, a particular document, or a particular video, etc. Contentitems may be predicted even more precisely, e.g., a particular portionof a document that has changed since the user last accessed thedocument, or a particular segment of a video, etc.

Content interest prediction may be performed based on historical datareflective of a user's content consumption spanning a long period oftime, e.g., days, weeks, months, etc. In one specific example, contentinterest prediction module 104 may predict that a user is interested inweather forecasts for a particular city based on data showing that theuser has consistently retrieved such forecasts over a period of time.

Content interest prediction may also be performed based on real-time ornear real-time data, e.g., a user's activity in the last few minutes orseconds. In one specific example, content interest prediction module 104may predict that a user is interested in a particular segment of a videobased on data showing that the user is currently watching a precedingsegment of that video.

Content interest prediction may take into account the user's predictedsituation, as may be provided by mobility prediction module 102. Forexample, content interests may be predicted based on the user'slocation, activity, whether the user is at work or at home, weatherconditions at the user's location, etc.

In one specific example, a user at work receives an alarm indicating analarm condition at her home (e.g., a washing machine leak), and beginstraveling home to deal with the alarm condition. In this example,mobility prediction module 102 may receive data indicating the alarmcondition, and indicating that the user is traveling on a route towardshome. On this basis of this data, mobility prediction module 104 maypredict the user's situation, e.g., that the user is returning home tofix her washing machine. Content interest prediction module 104 may usethis predicted situation to predict content items expected to be ofinterest to the user upon returning home, e.g., content items relatingto alarm information, repair information, safety information, mechaniccontact information, all of which may be assigned high priority giventhe urgency of the situation. These content items may then betransmitted to a child node 150 proximate the user's home.

The user's predicted situation may also include status information ofthe user's device, e.g., device state, battery level, programs running,commands received, etc.

Content interest prediction may also be performed based on informationregarding other users who may be associated with the other user, e.g.,friends, family, etc. In an embodiment, content interest predictionmodule 104 may obtain information regarding such other users by way ofthe particular user's contact list, which may be retrieved from mobiledevice 200 or a remote server, e.g., a social media platform.

Content interest prediction module 104 may obtain various data relatingto such other users including, for example, historical data indicatingcontent accessed by such other users, real-time or near real-time dataindicating content being accessed by such other users, real-time or nearreal-time data indicating a current situation (e.g., location, activity,etc.) of such other users. In an embodiment, content interest predictionmodule 104 may receive such data from a node in system 10 instantiatedfor one of the other users.

In one specific example, content interest prediction module 104 adds aparticular video to the user's content list upon determining that afriend or family member of the user has accessed that video.

Content interest prediction module 104 may assess a degree of affinitybetween the particular user and particular other users, and weighbehavior data of other users based on the degree of affinity. The degreeof affinity may be determined from data regarding frequency of contact,shared interest, shared demographics, or the like.

In an embodiment, content interest prediction module 104 may receivenotifications from one or more content data sources that updated contentdata is available, e.g., that content data previously-accessed by theuser or previously pre-fetched for the user has been updated. In suchcases, content interest prediction module 104 may assess whether theupdated content data is expected to be of interest to the user. If so,the updated content data may be added to the content list, and may bepre-fetched by pre-fetch module 110 in manners detailed below. Theupdated content data may also be pre-fetched by a child node 150, e.g.,upon receiving a portion of the content list from root node 100. In anembodiment, root node 100 may send a notification to one or more childnodes 150 that the updated content data is available. In an embodiment,root node 100 may transmit a request to one or more child nodes topre-fetch the updated content data.

In an embodiment, content interest predictions module 104 may receivecontent interest predictions from one or more trusted entities, and suchcontent interest predictions may be added to the node's content listwithout scrutiny.

Content interest prediction module 104 generates a content list havingentries identifying content items expected to be of interest to theuser. Content interest prediction module 104 maintains this content listin a data store 118 a. Content interest prediction module 104 updatesthis content list in data store 118 a as new content interestpredictions are made. Content interest prediction module 104 alsoupdates the content list in data store 118 a as content list updates arereceived from other nodes, from the user's mobile device 200, or fromother entities.

Content finder module 106 is adapted to perform content discovery bysearching for locations of content items, as may be provided by contentinterest prediction module 104. For example, content finder module 106may search for content items by scanning various content data sources;content finder module 106 may also search for content in local cachesand caches in interconnected nodes, e.g., caching nodes.

Content finder module 106 updates the content list in data store 118 ato include locations of content items, as found. In some cases, multiplelocations for the same content may be found, and each location may bestored in the content list in data store 118 a.

Content finder module 106 may also include in the content list anindicator of whether authorization is required to access the content,e.g., when the content item is a bank statement. Content finder module106 may also include in the content list an indicator of whether paymentis required to access the content, e.g., when the content item is behinda paywall. Content finder module 106 may also include in the contentlist an indicator of network transmission characteristics associatedwith a particular location, e.g., latency, delay of access, data rate,cost, etc. The cost may, for example, be a network cost or a monetarycost.

Content list distribution module 108 is configured to distribute contentlist data to child nodes 150. Content list distribution module 108processes a list of content items predicted to be of interest to theuser, e.g., as stored in data store 118 a, to assign priorities toidentified content items. Priorities may be determined based on thepredicted future locations of the user. For example, for a predictedlocation, content list distribution module 108 may estimate a likelihood(e.g., calculate a numerical likelihood) that the user will beinterested in each content item and generate predictions of when (e.g.,short term, long term, within time T₁, between T₁ and T₂, between T₂ andT₃) the user will be interested in each content item.

In an embodiment, the priorities for content items may be determinedbased on other aspects of a user's predicted situation, e.g., activitiesthat will be performed, applications that will be executed, etc.

In an embodiment, the priorities for content items may be determinedbased on network transmission characteristics associated with thelocations of content items, noted above. In an embodiment, thepriorities for content items may be determined based a cost of accessingthose content items, which may be, e.g., a monetary cost or a networkcost.

Content list distribution module 108 selects a subset of the list ofcontent items for transmitting to at least one of the child nodes 150.The subset may be selected based on the determined priorities, apredicted future location of the particular user, and the geographiclocation of that child node 150. In an embodiment, the subset may beselected based on other aspects of a user's predicted situation, e.g.,activities that will be performed, applications that will be executed,etc.

In an embodiment, content list distribution module 108 provides to eachchild node 150 the priority assigned to each content item transmitted tothat child node 150.

In an embodiment, content list distribution module 108 maintains arecord of previous content list data sent to each child node 150. Inthis embodiment, content finder module 106 sends content list updatesreflecting new/updated content items.

In an embodiment, when a content item is available at multiplelocations, as may be found by content finder module 106, content listdistribution module 108 may determine a preferred location to retrievethat data, based on predictions of the user's future location/situation.The preferred location may be indicated as such in the content listamong other alternate locations. In an embodiment, content listdistribution module 108 may select a preferred location based on networktransmission characteristics associated with a particular location(e.g., latency, delay of access, data rate, etc.).

In an embodiment, content list distribution module 108 may provide atleast part of the content list to trusted entities. Such trustedentities may use the content list data to offer/push content items tothe particular user. Such trusted entities may also assist inidentifying preferred or alternate locations for retrieving the contentitems.

As noted, in an embodiment, mobile device 200 may maintain its owncontent list. In this embodiment, content list distribution module 108also sends content list updates to mobile device 200 so that mobiledevice 200 may maintain its contact list in synchrony with the contactlist at root node 100.

Referring again to FIG. 2, pre-fetch module 110 is configured topre-fetch content data for a particular user. Pre-fetch module 100 maypre-fetch content data according to content items, locations andpriorities in the content list described above, as updated by contentinterest prediction module 104, content finder module 106, and contentlist distribution module 108. In an embodiment, pre-fetch module 100 mayuse a different content list, which may be provided by an externalentity. Pre-fetched content data may be stored at root node 100, e.g.,in content data store 118 b, for later transmission.

When multiple locations are available for pre-fetching the same contentdata, pre-fetch module 100 may select a preferred location, e.g., alocation close to the user, or a low-cost location.

In an embodiment, pre-fetch module 110 keeps track of any changes incontent items at a content data source, including availability of newcontent data. Such content items may be content items identified in thecontent list generated by content interest prediction module 104, oranother content list. In this way, pre-fetch module 110 may keep thepre-fetched data at root node 100 synchronized with the data at thecontent data source.

In one example, pre-fetch module 110 may poll a content server from timeto time to check for updates. Such polling may be conducted to a pre-setschedule, or may be conducted according to network resource availability(e.g., when there are low traffic conditions). In another example, acontent server may notify pre-fetch module 110 of changes in contentdata. In some cases, changes may be automatically pushed to pre-fetchmodule 110, or changes may be retrieved by pre-fetch module 110 uponreceiving notification. In some cases, pre-fetch module 110 maysubscribe to receive such notifications. In another example, a contentdata source may provide a schedule of when content data is expected tochange (e.g., when a new episode of a video program will be madeavailable for download), and pre-fetch module 110 may retrieve the newcontent data according to the provided schedule.

In an embodiment, pre-fetch module 110 may pre-fetch content data from acontent data source at a time based on a prediction of when the userwill require that data. For example, if pre-fetch module 110 receives aprediction that a user will want to watch a particular video at aparticular time, pre-fetch module 110 may pre-fetch data for that videoat a time between when that video becomes available and the predictedviewing time. The particular time that the data is pre-fetched maydepend on various factors including, for example, congestion of thenetwork, e.g., link condition, costs, and the user's location.

Upon determining that particular content data has changed, pre-fetchmodule 110 may send a notification to one or more of child nodes 150.Pre-fetch module 110 may also send a notification to mobile device 200.In an embodiment, child nodes 150 and/or device 200 are notifiedimmediately of such changes.

Upon receipt of such notifications, child nodes 150 and/or device 200may optionally retrieve some or all of the changed content data. Thecontent data may be retrieved from root node 100 or from the contentdata source.

In one specific example, device 200 may be configured to monitortemperatures at one or more locations (e.g., computer server rooms, foodcold storage rooms, etc.), and to perform a pre-programmed action (e.g.,raise an alarm) when a monitored temperature deviates from an expectedrange. Accordingly, device 200 may be configured to obtain temperaturesensor data from a monitored location. Until that sensor data changesfrom its last pre-fetched state, there is no need to pre-fetch new data.When a change occurs, as may be determined according to any of themanners described above, device 200 may receive notification of thechange. At that time, device 200 may pre-fetch the new sensor data.

In an embodiment, in lieu of pre-fetching certain content data for acontent item, pre-fetch module 110 may transmit a request to a childnode 150 to pre-fetch that content data at that child node 150.Pre-fetch module 110 may issue such a request, for example, when acontent item is found at a location closer to that child node 150. Therequest may include an identifier of the content item to be pre-fetched.The identifier may include a location of the content item.

Pre-authorization module 112 is configured to establish connections withinterconnected servers which require user/device authorization.Pre-authorization module 112 may maintain user or device credentials forsuch servers and present such credentials to establish authenticatedconnections. Content finder module 106 may use such authenticatedconnections to find content located at the interconnected servers.Pre-fetch module 110 may use such authenticated connections to pre-fetchdata from the interconnected servers. Upload module 116 may use suchauthenticated connections to upload data to the interconnected servers,as detailed below.

In an embodiment, pre-authorization module 112 establishes an authorizedconnection with an interconnected server based on a predicted user needfor the authorized connection, e.g., to download or upload data. In thisway, delay associated with establishing such connections may be avoidedwhen the authorized connection is needed.

In an embodiments, pre-authorization module 112 may be adapted tomaintain the connection, e.g., by periodic transmission of keep-alivesignals. In this way, a connection may be maintained on behalf of theuser even if the user's mobile device 200 loses connectivity.

Content data distribution module 114 is configured to transmitpre-fetched content data. Pre-fetched content data may be retrieved fromcontent data store 118 b for transmission. Content data distributionmodule 114 may, for example, transmit parts of pre-fetched content toone or more of child nodes 150. Such content data may be stored at achild node 150 until needed at the user's mobile device 200.

Content data distribution module 114 assigns priorities to parts of thepre-fetched content data. Priorities may be determined based on thepredicted future locations of the user. For example, for a predictedlocation, content data distribution module 114 may estimate a likelihood(e.g., calculate a numerical likelihood) that the part of thepre-fetched content data will be needed at the user's mobile device 200,and when (e.g., how soon) that data will be needed (e.g., short term,long term, within time T₁, between T₁ and T₂, between T₂ and T₃).

In an embodiment, the priorities for parts of pre-fetched content datamay be determined based on other aspects of a user's predictedsituation, e.g., activities that will be performed, applications thatwill be executed, etc.

The particular part of the pre-fetched content data transmitted to eachchild node 150 may be based on the determined priorities, the locationof the child node 150, the predicted location of the user, and thepredicted content data needs of the user when at the predicted location.In an embodiment, the portion may be selected based on other aspects ofa user's predicted situation, e.g., activities that will be performed,applications that will be executed, etc. In this way, pre-fetchedcontent data is sent towards a location or locations proximate to wherethe user is expected to be when that data will be needed.

The particular part of the pre-fetched content data transmitted to eachchild node 150 may also be based on other factors including, forexample, the quantity of the pre-fetched content data expected to beconsumed at device 200 while proximate a particular child node 150, howsoon content data will be needed at device 200, a cost or a metric ofnetwork conditions associated with data communication between the rootnode 100 and a particular child node 150, or a cost or a metric ofnetwork conditions associated with data communication between aparticular child node 150 and device 200.

The particular part of the pre-fetched content data transmitted to eachchild node 150 may be also be based on the data transmissioncharacteristics associated with that child node 150, e.g., transmissioncosts, data rate, capacity, latency, etc., as noted above. Such datatransmission characteristics may be received from the child nodes 150,or from monitors residing in a network 8. The particular part of thepre-fetched content data transmitted to each child node 150 may also bebased on the transmission preferences of the user, or of an applicationexecuting at the user's mobile device, as noted above.

The cost may, for example, be a monetary cost or a network costassociated with data communication with a particular child node 150. Themetric of network conditions may, for example, reflect a data rate,latency, capacity, congestion state, or load of particular link(s) ornodes. The metric of network conditions may, for example, be a Qualityof Experience metric associated with data communication with aparticular child node 150.

In an embodiment, the particular part of the pre-fetched content datatransmitted to each child node 150 may take into account other trafficbeing transmitted through networks 8, e.g., traffic generated by otherusers.

The particular part of the pre-fetched content data transmitted to eachchild node 150 may be based on a combination the factors noted above.

FIG. 3 depicts an example set of pre-fetched content data 120, 122, 124,126, 128, and so on, as may be stored in data store 118 b. Content datain this set are depicted in order of priority, e.g., 1, 2, 3, 4, 5, 6,and so on. As depicted, for this example set of content data, a subsetincluding content data 120 and 122 is selected for transmission to childnode 150-1. This subset of content data is expected be required at theuser's mobile device 200 when the user is proximate to child node 150-1.Another subset including content data 122, 124, and 126 is selected fortransmission to child node 150-2. This subset of content data isexpected to be required at device 200 when the user is proximate tochild node 150-2.

As depicted, although the subsets of pre-fetched content data sent tochild nodes 150 may differ, the subsets may overlap. Thus, for example,some pre-fetched content data (e.g., content data 122) may be sent tomultiple child nodes. In some cases, the same subset of pre-fetchedcontent data may be sent to multiple child nodes. In some cases, thesubset of pre-fetched content data sent to a child node 150 may have nooverlap with any subset of pre-fetched content data sent to anotherchild node 150. Further, a child node 150 may pre-fetch additional data.

Consequently, a child node 150 may maintain pre-fetched content datathat is unique to that child node 150, or that is identical to datamaintained at another child node 150. When the data is unique, it may ormay not include data that overlaps with data stored at another childnode 150.

Some parts of the content data (e.g., data 128) may be transmitted to nochild nodes. Such content data may include, for example, data notexpected to be needed at the user's device in the near future.

In an embodiment, content data distribution module 114 may divide largecontent items into a plurality of portions and transmit portions tomultiple child nodes 150, or at separate times (e.g., in separatesubsets). Dividing a content item may be desirable, for example, whenlink capacity or storage capacity at a child node 150 is constrained.The portions of the content item may be transmitted to the device 200from the multiple child nodes 150. Device 200 may reassemble the contentitem from received portions, or may use the content item portion byportion (e.g., as may be the case with streaming video). The contentitem may also be reassembled at a child node 150, e.g., when theportions of the content item are sent at separate times to the samechild node 150.

In an embodiment, content data distribution module 114 maintains arecord of previous pre-fetched content data sent to each child node 150.In this embodiment, pre-fetch module 110 sends content data updatesreflecting new/updated content data. For example, updates may take theform of a data delta or difference from a previous update. In anembodiment, new/updated content data may be compressed beforetransmission, and may be decompressed at a child node 150 or at mobiledevice 200.

In an embodiment, parts or all of the pre-fetched content data may alsobe transmitted directly to the user's mobile device 200.

Upload module 116 is configured to upload user data to interconnectedservers. User data may be received from the user's mobile device 200,either directly, or by way of child nodes 150. Received user data may betemporarily stored in upload data store 118 c before it is transmittedto an interconnected server. User data may, for example, include dataused to obtain access to further content data.

In an embodiment, user data for upload may be provided to mobilityprediction module 102, to predict the user's situation/location based onthe user data. In an embodiment, user data for upload may be provided tocontent interest prediction module 104, to predict the user's contentinterests based on the user data.

In an embodiment, user data for upload may include indicators of contentdata stored at the mobile device 200. Nodes 100 and 150 may process thatdata, for example, to identify content that does not need to betransmitted to device 200 and therefore should not be pre-fetched.Indications of content data stored at the mobile device 200 may beshared with other users or devices, for retrieval of content from mobiledevice 200.

In an embodiment, root node 100 may determine that no child node 150exists at a location suitably proximate a predicted future location ofthe user, and may cause a new child node 150 to be instantiated. Contentlist distribution module 108 may then transmit content list data to thenewly instantiated child node. Similarly, content data distributionmodule 114 may then transmit pre-fetched content data to the newlyinstantiated child node.

Each of child nodes 150 may be configured to have some or all of themodules and data stores of root node 100, as depicted in FIG. 2.

As noted, each child node 150 maintains its own content list. Each childnode may include a content list data store 118 a to store content listdata. Each node 150 populates data store 118 a with content list dataand any updates received.

In an embodiment, each child node 150 may include a mobility predictionmodule to perform mobility prediction in manners similar to root node100, as described above. A child node 150 may have access to data usefulfor such prediction that are not available to root node 100, or may haveaccess to such data sooner than root node 100, e.g., by virtue of beinglocated closer to the user, or being made aware of mobility of multipleusers in proximity to the child node 150. In one example, a child node150 may receive sensor data from the user's mobile device 200 beforeroot node 100. In another example, a child node 150 may be able toaccess local traffic data or local weather data for its particulargeographic location. So, in some cases, a child node 150 may able togenerate more accurate or more timely user mobility predictions.

In this embodiment, each child node 150 may exchange user mobilityprediction updates with root node 100. Such updates may include data forpredicting user mobility or any predictions that have been made. In thisway, predictions at each node may take advantage of available datarelating to user locations or user situations.

In an embodiment, each child node 150 may re-assess the priority ofcontent items in its content list, as received from root node 100. Forexample, child node 150 may re-assess the priority of content itemsbased on mobility predictions made at that node, and priorities may beupdated. The child node 150 may use such re-assessed priorities todetermine what parts of the content list or any content data stored atthe node are transmitted to other nodes or to device 200, and when.

Data reflective of updated priorities determined by child node 150 maybe transmitted to root node 100. Such data may also be sent to device200.

In an embodiment, each child node 150 may determine updates to itscontent list. For example, each child node 150 may include a contentinterest prediction module 102 to identify additional content items thatmay be of interest to the user, in manners similar to root node 100, asdescribed above. Content interest predictions performed at each childnode 150 may take into account any user mobility predictions generatedat that child node 150 or otherwise obtained at that child node 150.

Each child node 150 may also include a content finder module 106 to findlocations, e.g., including alternate locations, of content itemsidentified in its content list. Each child node 150 may search within alocal network neighborhood, for example, in its local cache or inneighboring caching nodes, which may not have been searched by root node100. So, locations for content items closer to the user may be found.

A child node 150 may send content list updates, e.g., reflecting newpredictions or locations, to root node 100. A child node 150 may alsosend content list updates to mobile device 200.

In an embodiment, a child node 150 includes a pre-fetch module 110 topre-fetch content data based on its content list, in manners similar toroot node 100, as described above. Pre-fetched content data may bestored at the child node 150, e.g., in a content data store 118 b, forsubsequent transmission to mobile device 200. Any pre-fetched contentdata received from root node 100 may also be stored in content datastore 118 b for subsequent transmission to mobile device 200 or toanother node. A child node 150 may pre-fetch particular content dataupon receiving a request from root node 100.

Conveniently, in this embodiment, child nodes 150 receive pre-fetchedcontent data from a root node 100. Such data received from root node 100need not be retrieved by a child node from a remote content data source.

Each child node 150 may assign priorities to pre-fetched content datafor subsequent transmission in manners similar to root node 100, asdescribed above. Prioritization at the child node 150 may take intoaccount any user mobility predictions made at that node.

As noted, mobile device 200 may maintain its own content list, and keepthis content list synchronized with the content list at root node 100.Mobile device 200 may generate new content list data to reflectinterests predicted or otherwise determined at the mobile device, andupdate its content list accordingly. For example, as the user of mobiledevice 200 accesses content, mobile device 200 may identify relatedcontent items and update its content list to include such relatedcontent items.

Mobile device 200 may also search for locations of content items, e.g.,in a local cache or within a local area network. Mobile device 200 mayinclude such locations in the content list and update one or more of thenodes (e.g., root node 100 and child nodes 150).

The content list at mobile device 200 may be updated by the user, a useragent, an application executing at device 200. The mobile device maytransmit a content list update to one or more of the nodes (e.g., rootnode 100 and child nodes 150).

In one specific example, a user may be driving a car under icy roadconditions. Mobile device 200 may receive data indicating that the useris driving (e.g., from onboard accelerometer or GPS sensor readings) anddata indicating local weather conditions. On the basis of this data,mobile device 200 may predict that the user will be interested inparticular content, e.g., driving instructions for icy road conditions.Mobile device 200 may assign a high priority to this content item giventhat the driver is currently driving. This content interest may beupdated to child nodes 150 proximate the driver's route.

In another specific example, a soccer player traveling to a soccertournament may indicate that she is interested in information relatingto a particular position or weaknesses of the opposing team. Thiscontent interest may be distributed to child nodes 150 proximate thesoccer field.

This content interest may trigger the same interest being added tocontent lists of other users, e.g., by a content interest predictionmodule 104 servicing those other users. Such other users may, forexample, be users having similar interest profiles (e.g., a player whoplays the same position) or users in close proximity to the soccerplayer (e.g., other players traveling to the soccer tournament).

In an embodiment, a mobile device 200 may share its content list withother mobile devices. Such other mobile devices may, for example, bemobile devices operated by friends, family or other trusted users. Suchother mobile devices may be interconnected with device 200 by, e.g., alocal area network, or a virtual local area network.

Mobile device 200 may share all or part of a content list withparticular other users or other devices. For example, a shared part of acontent list may relate to specific interest categories. Sharing acontent list may facilitate retrieval of content data of interest to agroup of users or devices, e.g., by one or more of the members of thegroup, to be shared amongst the group. Mobile device 200 may share allof part of a content list by way of root node 100 or one or more ofchild nodes 150.

FIG. 4 depicts a data communication system 10, according to anotherembodiment. Content discovery system 20 differs from content discoverysystem 10 in that whereas system 10 includes nodes organizedhierarchically into two levels (root node 100 at one level, and childnodes 150 below), system 20 includes nodes organized hierarchically intofour levels.

In particular, system 20 includes a root node 100, child nodes 130 at alevel below the root node 100, child nodes 140 at a level below childnodes 130, and child nodes 150 at a level below child nodes 140. Each ofroot node 100, child nodes 130, 140, and 150 are interconnected by atleast one data communication network 8.

Root node 100 of system 20 may function in manners substantially similarto that described above for root node 100 of system 10. Each of childnodes 130, 140, and 150 may function in manners substantially similar tothat described above for child node 150 system 10.

However, unlike in system 10, root node 100 does not send pre-fetchedcontent data, i.e., content data updates, directly to child node 150.Rather, root node 100 transmits content data updates to child nodes 130;each child node 130 sends content data updates to its interconnectedchild nodes 140; each child node 140 sends content data updates to itsinterconnected child nodes 150. Finally, child node 150 may transmitcontent data updates to mobile device 200.

Further, unlike in system 10, root node 100 does not exchange data(e.g., content list updates, content list priority updates, usermobility prediction updates) directly with child node 150. Rather, rootnode 100 exchanges such data with child nodes 130; each child node 130exchanges such data with its interconnected child nodes 140; each childnode 140 exchanges such data with its interconnected child nodes 150.Finally, each child node 150 may exchange such data with device 200.

FIG. 5 illustrates the propagation of content data updates 138 forpre-fetched content data down the node hierarchy of system 20 towardspredicted future locations of mobile device 200. Similarly, FIG. 5illustrates example propagation of user mobility prediction updates 132,content list updates 134, and content list priority updates 136, up anddown the node hierarchy of system 20 towards predicted future locationsof mobile device 200.

Nodes in descending levels of the hierarchy may have progressivelysmaller geographic scope of responsibility. For example, while root node100 may maintain pre-fetched content data for any content item expectedto be of interest to the particular user, a node 130 may maintainpre-fetched content data relevant for an assigned geographic region thatthe user is expected to move within or travel through. So, root node 100selects a portion of its pre-fetched content data for transmission to anode 130 that includes content data expected to be needed at device 200while the user is within the geographic region assigned to that node130. The limited geographic scope of a node 130 may also limit thetemporal scope of the pre-fetched content data maintained at the node130. For example, node 130 may maintain pre-fetched content data that isexpected to be needed at device 200 during the time period that the useris expected to be within the geographic region assigned to node 130.

Moving down the hierarchy, each node 140 may maintain pre-fetchedcontent data relevant for a subregion of the region assigned to itsparent node 130. So, a node 130 selects a portion of its pre-fetchedcontent data for transmission to a node 140 that includes content dataexpected to be needed at device 200 while the user is within thesubregion assigned to that node 140. Each node 150 may maintainpre-fetched content data relevant for an even smaller geographic areathat is part of the subregion assigned to its parent node 140. So, anode 140 selects a portion of its pre-fetched content data fortransmission to a node 150 that includes content data expected to beneeded at device 200 while the user is within the area assigned to thatnode 150.

Nodes in descending levels of the hierarchy may be progressively closerto the user's location. Thus, nodes at each descending level may haveaccess to data for making more accurate or more timely user mobilitypredictions, and may be able to perform more accurate prioritization ofcontent items and/or pre-fetched content data. As shown in FIG. 5, usermobility prediction updates and priority updates may be propagated fromthe lower levels upwards.

Nodes at each level of the hierarchy may cause a new node in a levelbelow it to be instantiated to provide a new node at a locationcorresponding to a predicted future location of the user. Similarly,nodes at each level of the hierarchy may cause a node in a level belowit to be deactivated or removed when it is no longer required.

As depicted, each of nodes 100, 130, and 140 is interconnected with oneor more traffic controllers 12. Additionally, each node 150 may beinterconnected with one or more traffic controllers 12. Each trafficcontroller 12 is configured to perform traffic engineering functions tocontrol the transmission of traffic (e.g., routing and scheduling) in arespective data communication network 8. So, transmission of traffic insystem 100, e.g., between nodes, or from nodes to a user's mobiledevice, is controlled by the one or more traffic controllers 12, andtransmission requests are sent to the one or more traffic controllers12. In an embodiment, one or more of nodes 100, 130, 140, and 150 may benot connected with a traffic controller 12, and transmission of trafficfor such nodes may be controlled by the node itself or controlled byanother controller (e.g., at another node). In an embodiment, a trafficcontroller 12 may be a Software-Defined Networking (SDN) controller. Inan embodiment, a traffic controller 12 may be another type of networkcontroller.

Each of nodes 100, 130, 140, and 150 may be optionally interconnectedwith one or more cache nodes 16 by way of one or more data communicationnetworks 8. Each of nodes 100, 130, 140, and 150 may optionally includeone or more local caches 18. Caching nodes 16 and local caches 18 mayeach contain cached content data. The cached content data may, forexample, be data accessed in the past by the particular user, or otherusers. The cached content data may, for example, be data frequentlyaccessed e.g., in a particular geographic region or in a particularnetwork 8. Nodes may search within cache nodes 16 and local caches 18 toidentify content items of interest, or to find locations for identifiedcontent items. Content may be pre-fetched from any of these cache nodes16 or local caches 18.

In an embodiment, each of nodes 100, 130, 140, and 150 may function as avirtualized version of mobile device 200, and present itself as mobiledevice 200 to network components, e.g., traffic controller 12. In onespecific example, a node may present itself as mobile device 200 inorder to obtain authorized access to network resources on behalf ofmobile device 200. In another specific example, a node may track aspectsof the state of mobile device 200, e.g., its power level, and distancefrom a power source. Such information may be used, for example, tochange the priority of particular content data or particular contentlist data to be transmitted to the device.

In an embodiment, each of nodes 100, 130, 140, and 150 may be associatedwith other network functionalities, which may be assigned based on thelevel of that node in the hierarchy. For example, nodes 130 may functionas regional connectivity managers that facilitate tracking of a mobiledevice 200, and interact with traffic controller 12 on behalf of mobiledevice 200. For example, nodes 140 may function as local connectivitymanagers. Nodes 140 may also function as default gateways for the mobiledevice 200. So, node 140 may maintain data regarding future routingdemands of device 200, e.g., based on content data that has beenpre-fetched and is expected to be transmitted to device 200. Suchrouting demands may be provided to traffic controller 12.

Like the nodes 150, the nodes 130 and/or 140 may be instantiated toservice a geographical area that varies according to a particular user'slocation. For example, this geographical area may migrate with theparticular user as that user moves from location to location.

In one specific example, a node 100 may be located in the cloud. Aparticular node 130 may be instantiated in the particular user's homecity, e.g., at a regional gateway. A particular node 140 may beinstantiated proximate the user's home, e.g., when the user is at home.Another node 140 may instantiated proximate the user's work, e.g., whenthe user is at work. When the user moves throughout the city, node 130may be maintained in position, and new nodes 140 and 150 may beinstantiated at various locations proximate to the user's changinglocations. When the user leaves the city, e.g., by way of an airplane, anew node 130 may be instantiated, e.g., at a satellite, to serve theuser while in transit. Nodes 140 and 150 may, for example, beinstantiated within the airplane. When the user arrives at a new city, anew node 130 may be instantiated for the user in that new city.Similarly, new nodes 140 and 150 may also be instantiated for the userin the new city. New nodes 140 and 150 may be instantiated to service ageographical area that varies according to the user's particularlocation in the new city.

Each of nodes 130, 140, 150 may be referred to herein as child nodes ofroot node 100. However, a node 140 may also be referred to as a childnode of a node 130, and a grandchild node of root node 100. Similarly, anode 150 may also be referred to as a child node of a node 140, agrandchild node of a node 130, and a great grandchild node of root node100. Conversely, each of nodes 100, 130, and 140 may be referred to as aparent node, grandparent node, or great grandparent node of theirrespective child nodes, grandchild nodes, and great grandchild nodes.

Nodes organized hierarchically into two levels and four levels have beenshown in the depicted embodiments. However, in other embodiment, theremay be a fewer or greater number of levels.

Further, in the depicted embodiments, the nodes are interconnectedaccording to a tree topology. However, in other embodiments, the nodesmay be interconnected according to a different topology (e.g., meshtopology). For example, each child node may be interconnected withmultiple parent nodes, and may exchange data (e.g., content listupdates, user mobility prediction updates, content data updates, etc.)with each of those parent nodes. Further, each child node may beinterconnected with other child nodes at the same level of the hierarchy(e.g., its sibling nodes), such that data may be exchanged between childnodes. In an embodiment, the nodes of system 10 may be interconnectedsuch that subsets of the nodes are arranged according to differenttopologies.

In some embodiments, information generated or obtained by system 10 ormobile device 200, e.g., user mobility predictions, alternate locationsof data, network transmission characteristics, when data is expected tobe needed, etc., may be provided to traffic controller 12. Trafficcontroller 12 may take into account all such information to route andschedule data traffic.

In some embodiments, storage and transmission of content interests andcontent data may take into account security requirements, e.g., toprotect confidential information or privacy. Such requirements may bespecified, e.g., for each content item, or each type of content item.For example, a high level of security may be specified for content itemsincluding sensitive data such as, for example, personal photographs orbank statements. Conversely, a low level of security may be specifiedfor publically available data such as, e.g., weather reports. Securityrequirements may be included in the content list, in association withparticular content items.

Portions of content lists or content data may also be classifiedaccording to their purpose, e.g., personal or work, and differentsecurity requirements may be specified based on this purpose.

Portions of content lists or content data may be stored in particularlocations and/or transmitted by way of particular links, depending onsecurity requirements. For example, portions of content lists or contentdata associated with a user's work may be stored in a secured corporateserver, and may be transmitted by way of secured VPN links.

In an embodiment, nodes may be instantiated at particular locations atleast partly based on security requirements, e.g., using particularsecured hardware or at particular secured network locations. In anembodiment, subsets of content lists and/or subsets of content data maybe selected at least partly based on security requirements, e.g., fortransmission to particular secured hardware or to particular securednetwork locations.

Content lists and content data may be encrypted during transmissionand/or storage.

The operation of the data communication systems disclosed herein may befurther described with reference to the flowcharts depicted in FIG. 6,FIG. 7, and FIG. 8.

FIG. 6 depicts an example method 600 that may be performed at a rootnode 100 or at any parent node to distribute data pre-fetched at theroot node 100 for a particular user, and content list data includingidentifiers of data expected to be of interest to the particular user,e.g., for pre-fetching at a child node. This data pre-fetched at rootnode 100 and the content list data may be referred to collectively aspre-fetch data. As will be appreciated, the order of the blocks is shownas an example only, and blocks may be performed in other suitableorders.

As depicted, operation begins at block 602. At block 602, root node 100performs user mobility prediction for a particular user, in mannersdescribed above.

At blocks 604 and 606, root node 100 obtains pre-fetch data fordistributing to child nodes. In particular, at root node 100 performscontent interest prediction to determine content expected to be ofinterest to the particular user. For example, root node 100 may generatecontent list data including one or more identifiers identifying dataexpected to be of interest to the particular user. In anotherembodiment, root node 100 may simply receive a list of content expectedto be of interest to the user from an external entity, such that contentinterest prediction at the root node 100 may be omitted.

At block 606, root node 100 optionally pre-fetches content expected tobe of interest to the particular user from at least one content datasource, e.g., by way of at least one network 8.

At block 608, root node 100 selects subset(s) of the pre-fetch data tobe transmitted to one or more of the child nodes. For example, root node100 may select a first subset of the pre-fetch data to be transmitted toa first child node, and select a second subset of the pre-fetch data tobe transmitted to a second child node. Each subset may be selected inthe manners described above. For example, a subset to be sent to a childnode may be selected based on at least a predicted future location ofthe user, and the geographic location of that child node. The selectedsubsets of the pre-fetch data are then transmitted to the each of therespective child nodes.

FIG. 7 depicts an example method 700 that may be performed at a rootnode 100 or at any parent node to instantiate a new child node (e.g., achild node 130, 140, or 150). As depicted, operation begins at block702.

At block 702, root node 100 determines whether a new child node isrequired, e.g., based on a prediction of a future location of the user.The prediction of the future location of the user may be, for example,obtained by root node 100 by performing user mobility prediction atblock 602 (FIG. 6), or received from device 200. A new child node may bedetermined to be required, for example, if there are no existing childnodes proximate the predicted future location. If a new child node isrequired, at block 704, a desired geographic location of the new childnode is selected based on the prediction of the future location of theuser, e.g., to be proximate the predicted future location. Selection ofthe geographic location of the new child node is also based on thelocations of devices available to function as the new child node. Atblock 706, a new child node is instantiated at the selected geographiclocation. For example, root node 100 may transmit a request to a deviceat the selected geographic location to function as the new child node.

Method 700 may be performed at root node 100 before, following, or inparallel with any of blocks 600 depicted in FIG. 6. For example, a newchild node may be instantiated before, after, or during pre-fetching ofcontent data.

One or more of method 600 or method 700 described above may be repeatedat root node 100, e.g., as new content data needs to be pre-fetched, oras a user's location changes, or as a user's predicted future locationchanges.

FIG. 8 depicts an example method 800 that may be performed at a childnode (e.g., a child node 130, 140, or 150). As depicted, operationbegins at block 802. At block 802, the child node receives pre-fetchedcontent data from its root node (e.g., root node 100).

At block 804, the child node may, optionally, pre-fetch additionalcontent data from one or more content data sources, by way of at leastone network 8.

In some cases, the child node may identify content expected to be ofinterest to the particular user, e.g., by performing content interestprediction, and then may pre-fetch additional content data based oncontent that it has identified.

In other cases, the child node may receive an identifier identifyingcontent expected to be of interest from the parent node. The identifiermay be included in the content list provided to the child node. Theidentifier may also be included in a request sent by the parent node topre-fetch particular content data.

In yet other cases, the child node may receive from mobile device 200 anidentifier identifying content expected to be of interest from mobiledevice 200. The identifier may be included in the content list providedto the child node. The identifier may also be included in a request sentby device 200 to pre-fetch particular content data.

At block 806, some or all of the pre-fetched data is transmitted tomobile device 200.

One or more of the blocks described above may be repeated at the childnode, e.g., as new pre-fetched content data is received from the parentnode.

As will be appreciated, the flowcharts depicted in FIG. 6, FIG. 7, andFIG. 8 each show simplified example operation of the systems describedherein, and other details (e.g., prediction of user situation, exchangeof content list data, uploading of data, etc.) have been omitted forclarity. The systems may operate in these and other manners.

Although embodiments have been described in the foregoing with referenceto mobile devices, the systems, methods and devices disclosed herein maybe applied to all manner of devices, e.g., vehicles, robots, machines,sensors, televisions, desktop computers, etc. Such devices need not bemobile. Such devices may be integrated with other devices or equipment.

Although embodiments have been described in the foregoing with referenceto example nodes having particular functionality, it will be appreciatedthat in some embodiments, the functionality described for one examplenode may be distributed over multiple nodes, which may each beimplemented in hardware, software, or a combination thereof. In someembodiments, multiples nodes may be implemented using shared hardware orsoftware.

Embodiments have been described in the foregoing with reference to auser. As will be appreciated, in some embodiments, the user need not bea human being. Rather, the user may, for example, be a device, amachine, or a software application.

In one specific example, the user may be a robot, and content ofinterest for the robot may relate to potential kinematic designs. Suchcontent may be retrieved by the robot, for example, to plan upcomingmovements.

In another specific example, the user may be a controller for anelectronic billboard, and content of interest may be advertisements.Such content may be retrieved, for example, in response to the presenceof particular users.

In another specific example, the user may be a controller for aself-driving vehicle, and content of interest may be road conditions.Such content may be retrieved, for example, to adapt to such roadconditions.

FIG. 9 is a schematic diagram of an example computing device 900 thatmay be adapted to function as any of the nodes described herein. Thecomputing device may be any network-enabled computing device such as,e.g., a server-class computer, or a personal computer, a router, aswitch, an access point, etc.

In the depicted embodiment, computing device 900 includes at least oneprocessor 902, memory 904, at least one I/O interface 906, and at leastone network interface 908.

Processor 902 may be any type of processor, such as, for example, anytype of general-purpose microprocessor or microcontroller (e.g., anIntel™ x86, PowerPC™ ARM™ processor, or the like), a digital signalprocessing (DSP) processor, an integrated circuit, a field programmablegate array (FPGA), or any combination thereof.

Memory 904 may include a suitable combination of any type of computermemory that is located either internally or externally such as, forexample, random-access memory (RAM), read-only memory (ROM), compactdisc read-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM), flashmemory, or the like.

I/O interface 906 enables device 900 to interconnect with input andoutput devices, e.g., peripheral devices or external storage devices.

Network interface 908 enables device 900 to communicate with othercomponents, e.g., other nodes, and perform other computing applicationsby connecting to a network such as one or more of networks 8.

Embodiments disclosed herein may be implemented by using hardware onlyor by using software and a necessary universal hardware platform. Basedon such understandings, the technical solution may be embodied in theform of a software product. The software product may be stored in anon-volatile or non-transitory storage medium, which can be a compactdisk read-only memory (CD-ROM), USB flash disk, a solid-state drive, ora removable hard disk. The software product includes a number ofinstructions that enable a computer device (personal computer, server,or network device) to execute the methods provided in the embodiments.

Program code, which may be stored in memory 904, may be applied to inputdata to perform the functions described herein and to generate outputinformation. The output information may be applied to one or more outputdevices. In some embodiments, the communication interface with suchoutput devices may be a network communication interface (e.g., interface908). In embodiments in which elements may be combined, thecommunication interface may be a software communication interface, suchas those for inter-process communication. In still other embodiments,there may be a combination of communication interfaces implemented ashardware, software, and combination thereof.

Each computer program may be stored on a storage media or a device(e.g., ROM, magnetic disk, optical disc, solid-state drive), readable bya general or special purpose programmable computer, for configuring andoperating the computer when the storage media or device is read by thecomputer to perform the procedures described herein. Embodiments of thesystem may also be considered to be implemented as a non-transitorycomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform the functions describedherein.

Furthermore, the systems and methods of the described embodiments arecapable of being distributed in a computer program product including aphysical, non-transitory computer readable medium that bears computerusable instructions for one or more processors. The medium may beprovided in various forms, including magnetic and electronic storagemedia, such as one or more diskettes, compact disks, tapes, chips, orthe like. The medium may be configured to provide memory that isvolatile or non-volatile. Non-transitory computer-readable media mayinclude all computer-readable media, with the exception being atransitory, propagating signal. The term non-transitory is not intendedto exclude computer readable media such as primary memory, volatilememory, RAM and so on, where the data stored thereon may only betemporarily stored. The computer useable instructions may also be invarious forms, including compiled and non-compiled code.

It will be noted that servers, services, interfaces, portals, platforms,or other systems formed from hardware devices can be used. It should beappreciated that the use of such terms is deemed to represent one ormore devices having at least one processor configured to executesoftware instructions stored on a computer readable tangible,non-transitory medium. One should further appreciate the disclosedcomputer-based algorithms, processes, methods, or other types ofinstruction sets can be embodied as a computer program productcomprising a non-transitory, tangible computer readable media storingthe instructions that cause a processor to execute the disclosed steps.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

The embodiments described herein are implemented by physical computerhardware embodiments. The embodiments described herein provide usefulphysical machines and particularly configured computer hardwarearrangements of computing devices, servers, processors, memory,networks, for example. The embodiments described herein, for example,are directed to computer apparatuses, and methods implemented bycomputers through the processing and transformation of electronic datasignals.

The embodiments described herein may involve computing devices, servers,receivers, transmitters, processors, memory, display, networksparticularly configured to implement various acts. The embodimentsdescribed herein are directed to electronic machines adapted forprocessing and transforming electromagnetic signals which representvarious types of information. The embodiments described hereinpervasively and integrally relate to machines, and their uses; and theembodiments described herein have no meaning or practical applicabilityoutside their use with computer hardware, machines, a various hardwarecomponents.

Substituting the computing devices, servers, receivers, transmitters,processors, memory, display, or networks particularly configured toimplement various acts for non-physical hardware, using mental steps forexample, may substantially affect the way the embodiments work.

Such hardware limitations are clearly essential elements of theembodiments described herein, and they cannot be omitted or substitutedfor mental means without having a material effect on the operation andstructure of the embodiments described herein. The hardware is essentialto the embodiments described herein and is not merely used to performsteps expeditiously and in an efficient manner.

Although the present invention and its advantages have been described indetail, it should be understood that various changes, substitutions andalterations can be made herein without departing from the invention asdefined by the appended claims.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification. As one of ordinary skill in the art will readilyappreciate from the disclosure of the present invention, processes,machines, manufacture, compositions of matter, means, methods, or steps,presently existing or later to be developed, that perform substantiallythe same function or achieve substantially the same result as thecorresponding embodiments described herein may be utilized according tothe present invention. Accordingly, the appended claims are intended toinclude within their scope such processes, machines, manufacture,compositions of matter, means, methods, or steps.

As can be understood, the embodiments described above and illustratedare intended to be examples only. The scope is indicated by the appendedclaims.

What is claimed is:
 1. A method at a parent node for distributingpre-fetch data to at least two child nodes, the method comprising:storing a content prediction list identifying data expected to be ofinterest to a particular user, based on user activity; synchronizing thecontent prediction list and a local content prediction list at theclient device, based on user activity at the client device; obtaining,from at least one data source, by way of at least one data network,pre-fetch data comprising data identified in the content predictionlist; selecting a first subset and a second subset of the pre-fetch datafor transmission, respectively, to a first child node and a second childnode of the at least two child nodes, the selecting based on at least apredicted future location of the particular user and a respectivegeographic location of the first and second child nodes; andtransmitting the first subset and the second subset of the pre-fetchdata, respectively, to the first child node and the second child node byway of the at least one data network.
 2. The method of claim 1,comprising receiving transmissions indicative of user activity from theclient device, and generating entries of the content prediction listbased on the received transmissions.
 3. The method of claim 1, whereinsynchronizing the content list comprises receiving prediction data fromthe client device identifying data expected to be of interest to theparticular user, and updating the content prediction list based on thereceived prediction data.
 4. The method of claim 1, whereinsynchronizing the content list comprises sending prediction data from toclient device identifying data expected to be of interest to theparticular user, for updating the local content prediction list.
 5. Themethod of claim 1, further comprising: selecting a desired location asthe geographic location of a given one of the first and second childnodes based on at least the predicted future location of the particularuser.
 6. The method of claim 1, further comprising: transmitting arequest to a device at the desired location to function as the given oneof the first and second child nodes.
 7. The method of claim 1, furthercomprising: determining when previously pre-fetched data is updated atthe at least one data source, pre-fetching the updated data andtransmitting data to at least one of the child nodes for updating dataat the at least one of the child nodes.
 8. The method of claim 1,further comprising: transmitting a request to at least one of the childnodes to pre-fetch the updated data.
 9. The method of claim 1, whereinthe selecting is based on at least one of: network transmissioncharacteristics associated with data communication with at least one ofthe child nodes, a prediction of how soon data will be needed, contentquantity, user cost preferences, traffic associated with other users,and data security requirements.
 10. The method of claim 9, wherein thenetwork transmission characteristics comprise at least one of: a datarate, a latency, a capacity, a congestion state, and a cost.
 11. Themethod of claim 1, wherein the at least one data source includes acache, and the data pre-fetched at the parent node comprises data cachedat the at least one data source.
 12. The method of claim 1, wherein thesynchronizing comprises synchronizing with a content list at a deviceinterconnected with the client device over a local area network.
 13. Themethod of claim 1, wherein the synchronizing comprises updating thelocal content prediction list based on the content prediction list. 14.The method of claim 1, wherein the synchronizing comprises updating thecontent prediction list based on the local content prediction list. 15.The method of claim 1, comprising storing content prediction lists ateach of a plurality of nodes in a hierarchy, wherein a child node in thehierarchy is closer to the location of the client device than itsrespective parent node, and the content prediction list at the childnode contains only part of the content prediction data at the parentnode.
 16. The method of claim 1, wherein the local content predictionlist is generated locally at the client device.
 17. A network node fordistributing pre-fetch data, the node comprising: a network interfacefor interconnection with at least two child nodes by at least one datanetwork; and at least one processor in communication with the networkinterface, the at least one processor configured to: store a contentprediction list identifying data expected to be of interest to aparticular user; synchronize the content prediction list and a localcontent prediction list at the client device, based on user activity atthe device; obtain, from at least one data source by way of at least onedata network pre-fetch data comprising data identified in the contentprediction list; select a first subset and a second subset of thepre-fetch data for transmission, respectively, to a first child node anda second child node of the at least two child nodes, the selecting basedon at least a predicted future location of the particular user and arespective geographic location of the first and second child nodes; andtransmit the first subset and the second subset of the pre-fetch data,respectively, to the first child node and the second child node by wayof the at least one data network.
 18. The network node of claim 17,wherein the at least one processor is configured to generate entries ofthe content prediction list based on transmissions indicative of useractivity received from the client device.
 19. The network node of claim17, wherein the at least one processor is configured to update thecontent prediction list based on prediction data received from theclient device, identifying data expected to be of interest to theparticular user.
 20. The network node of claim 17, wherein the at leastone processor is configured to select a desired location as thegeographic location of a given one of the first and second child nodesbased on at least the predicted future location of the particular user.21. The network node of claim 17, wherein the at least one processor isconfigured to transmit, by way of the network interface, a request to adevice at the desired location to function as the given one of the firstand second child nodes.
 22. The network node of claim 17, wherein the atleast one processor is configured to determine when previouslypre-fetched data is updated at the at least one data source, pre-fetchthe updated data and transmit data to at least one of the child nodesfor updating data at the at least one of the child nodes.
 23. Thenetwork node of claim 22, wherein the at least one processor isconfigured to transmit a request to at least one of the child nodes topre-fetch the updated data.
 24. The network node of claim 17, whereinthe selecting is based on at least one of: network transmissioncharacteristics associated with data communication with at least one ofthe child nodes, a prediction of how soon data will be needed, contentquantity, user cost preferences, traffic associated with other users,and data security requirements.
 25. The network node of claim 24,wherein the network transmission characteristics comprise at least oneof: a data rate, a latency, a capacity, a congestion state, and a cost.26. The network node of claim 17, wherein the at least one data sourceincludes a cache, and the data pre-fetched at the network node comprisesdata cached at the at least one data source.
 27. The network node ofclaim 17, wherein the processor is configured to synchronize the contentprediction list and the local content prediction list by updating thecontent prediction list based on the local content prediction list. 28.The network node of claim 17, wherein the processor is configured tosynchronize the content prediction list and the local content predictionlist by updating the local content prediction list based on the contentprediction list.
 29. The network node of claim 17, wherein the processoris configured to synchronize the content prediction list with a localcontent prediction list generated locally at the client device based onuser activity.